test-predict_bnns.R
In bnns: Bayesian Neural Network with 'Stan'

# # Running NN
#
# data <- data.frame(x1 = runif(10), x2 = runif(10), y = rnorm(10))
# model_f <- bnns(y ~ -1 + x1 + x2, data = data, L = 1, nodes = 2, act_fn = 2, iter = 1e1, warmup = 5, chains = 1, normalize = FALSE)
# # model_f_2 <- bnns(y ~ -1 + x1 + x2, data = data, L = 4, nodes = rep(2, 4), act_fn = 1:4, iter = 1e1, warmup = 5, chains = 1)
# new_data <- data.frame(x1 = runif(5), x2 = runif(5))
#
# # data_bin <- data.frame(x1 = runif(10), x2 = runif(10), y = sample(0:1, 10, replace = TRUE))
# # model_bin <- bnns(y ~ -1 + x1 + x2, data = data_bin, L = 4, nodes = rep(2, 4), act_fn = 1:4, iter = 1e1, warmup = 5, chains = 1, out_act_fn = 2)
#
# # data_cat <- data.frame(x1 = runif(10), x2 = runif(10), y = factor(sample(LETTERS[1:3], 10, replace = TRUE)))
# # model_cat <- bnns(y ~ -1 + x1 + x2, data = data_cat, L = 4, nodes = rep(2, 4), act_fn = 1:4, iter = 1e1, warmup = 5, chains = 1, out_act_fn = 3)
#
# # Redirect output for testing predictions
# capture_predictions <- function(object, newdata = NULL) {
#   predict.bnns(object, newdata = newdata)
# }
#
# # Test script for predict.bnns
# test_that("predict.bnns produces predictions for training data", {
#   predictions <- capture_predictions(model_f)
#
#   # Test that predictions have correct dimensions
#   expect_equal(dim(predictions)[1], 10) # 10 rows
#
#   # Test that predictions are numeric
#   expect_type(predictions, "double")
# })
#
# test_that("predict.bnns works with newdata", {
#   predictions <- capture_predictions(model_f, newdata = new_data)
#
#   # Test that predictions have correct dimensions
#   expect_equal(dim(predictions)[1], 5) # 5 rows
#
#   # Test that predictions are numeric
#   expect_type(predictions, "double")
# })
#
# # test_that("predict.bnns works with for more than 1 layer with different activation functions", {
# #   predictions <- capture_predictions(model_f_2, newdata = new_data)
# #
# #   # Test that predictions have correct dimensions
# #   expect_equal(dim(predictions)[1], 5) # 5 rows
# #
# #   # Test that predictions are numeric
# #   expect_type(predictions, "double")
# # })
# #
# # test_that("predict.bnns works with for more than 1 layer with different activation functions for the categorical case", {
# #   predictions <- capture_predictions(model_cat, newdata = new_data)
# #
# #   # Test that predictions have correct dimensions
# #   expect_equal(dim(predictions)[1], 5) # 5 rows
# #
# #   # Test that predictions are numeric
# #   expect_type(predictions, "double")
# # })
# #
# # test_that("predict.bnns works with for more than 1 layer with different activation functions for the binary case", {
# #   predictions <- capture_predictions(model_bin, newdata = new_data)
# #
# #   # Test that predictions have correct dimensions
# #   expect_equal(dim(predictions)[1], 5) # 5 rows
# #
# #   # Test that predictions are numeric
# #   expect_type(predictions, "double")
# # })